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Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Related Experiment Video

Updated: Sep 19, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Active Learning FEP Using 3D-QSAR for Prioritizing Bioisosteres in Medicinal Chemistry.

Venkata K Ramaswamy¹, Matthew Habgood¹, Mark D Mackey¹

¹Cresset, New Cambridge House, Bassingbourn Road, Litlington SG8 0SS, Cambridgeshire, United Kingdom.

ACS Medicinal Chemistry Letters

|June 18, 2025

Summary

This summary is machine-generated.

This study introduces an active learning workflow combining 3D-QSAR and binding free energy calculations to efficiently identify optimal bioisosteric replacements for drug discovery. This approach rapidly prioritizes molecules, saving time and resources in candidate optimization.

Keywords:

3D-QSAR FEP Spark active learning bioisostere

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Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

Bioisostere replacement is crucial for optimizing drug candidate potency and selectivity.
Efficiently selecting bioisosteres is key to successful drug discovery projects.
A large pool of potential bioisosteres requires effective prioritization methods.

Purpose of the Study:

To develop and demonstrate an active learning workflow for prioritizing bioisosteric replacements.
To combine 3D-QSAR and relative binding free energy calculations for enhanced prioritization.
To accelerate the identification of potent and selective drug candidates.

Main Methods:

Integration of 3D-quantitative structure-activity relationship (3D-QSAR) models.
Application of relative binding free energy (RBFE) calculations.
Development of an active learning workflow to iteratively prioritize molecules.

Main Results:

The workflow successfully prioritized bioisosteric replacements from a large pool.
Demonstrated rapid identification of strongest-binding bioisosteres on a human aldose reductase test case.
Achieved efficient prioritization with a modest computational cost.

Conclusions:

The combined 3D-QSAR and RBFE active learning workflow is effective for bioisostere prioritization in drug discovery.
This computational approach significantly enhances the efficiency of lead optimization.
The method offers a valuable tool for accelerating the discovery of new therapeutics.